ee.ImageCollection.mode
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با محاسبه رایج‌ترین مقدار در هر پیکسل در پشته همه باندهای منطبق، یک مجموعه تصویر را کاهش می‌دهد. گروه ها با نام مطابقت دارند.
| استفاده | برمی گرداند | ImageCollection. mode () | تصویر |
| استدلال | تایپ کنید | جزئیات | این: collection | ImageCollection | مجموعه تصاویر برای کاهش. |
نمونه ها
ویرایشگر کد (جاوا اسکریپت)
// Sentinel-2 image collection for July 2021 intersecting a point of interest.
// Reflectance, cloud probability, and scene classification bands are selected.
var col = ee.ImageCollection('COPERNICUS/S2_SR')
.filterDate('2021-07-01', '2021-08-01')
.filterBounds(ee.Geometry.Point(-122.373, 37.448))
.select('B.*|MSK_CLDPRB|SCL');
// Visualization parameters for reflectance RGB.
var visRefl = {
bands: ['B11', 'B8', 'B3'],
min: 0,
max: 4000
};
Map.setCenter(-122.373, 37.448, 9);
Map.addLayer(col, visRefl, 'Collection reference', false);
// Reduce the collection to a single image using a variety of methods.
var mean = col.mean();
Map.addLayer(mean, visRefl, 'Mean (B11, B8, B3)');
var median = col.median();
Map.addLayer(median, visRefl, 'Median (B11, B8, B3)');
var min = col.min();
Map.addLayer(min, visRefl, 'Min (B11, B8, B3)');
var max = col.max();
Map.addLayer(max, visRefl, 'Max (B11, B8, B3)');
var sum = col.sum();
Map.addLayer(sum,
{bands: ['MSK_CLDPRB'], min: 0, max: 500}, 'Sum (MSK_CLDPRB)');
var product = col.product();
Map.addLayer(product,
{bands: ['MSK_CLDPRB'], min: 0, max: 1e10}, 'Product (MSK_CLDPRB)');
// ee.ImageCollection.mode returns the most common value. If multiple mode
// values occur, the minimum mode value is returned.
var mode = col.mode();
Map.addLayer(mode, {bands: ['SCL'], min: 1, max: 11}, 'Mode (pixel class)');
// ee.ImageCollection.count returns the frequency of valid observations. Here,
// image pixels are masked based on cloud probability to add valid observation
// variability to the collection. Note that pixels with no valid observations
// are masked out of the returned image.
var notCloudCol = col.map(function(img) {
return img.updateMask(img.select('MSK_CLDPRB').lte(10));
});
var count = notCloudCol.count();
Map.addLayer(count, {min: 1, max: 5}, 'Count (not cloud observations)');
// ee.ImageCollection.mosaic composites images according to their position in
// the collection (priority is last to first) and pixel mask status, where
// invalid (mask value 0) pixels are filled by preceding valid (mask value >0)
// pixels.
var mosaic = notCloudCol.mosaic();
Map.addLayer(mosaic, visRefl, 'Mosaic (B11, B8, B3)'); راه اندازی پایتون
برای اطلاعات در مورد API پایتون و استفاده از geemap برای توسعه تعاملی به صفحه محیط پایتون مراجعه کنید.
import ee
import geemap.core as geemap
کولب (پایتون)
# Sentinel-2 image collection for July 2021 intersecting a point of interest.
# Reflectance, cloud probability, and scene classification bands are selected.
col = (
ee.ImageCollection('COPERNICUS/S2_SR')
.filterDate('2021-07-01', '2021-08-01')
.filterBounds(ee.Geometry.Point(-122.373, 37.448))
.select('B.*|MSK_CLDPRB|SCL')
)
# Visualization parameters for reflectance RGB.
vis_refl = {'bands': ['B11', 'B8', 'B3'], 'min': 0, 'max': 4000}
m = geemap.Map()
m.set_center(-122.373, 37.448, 9)
m.add_layer(col, vis_refl, 'Collection reference', False)
# Reduce the collection to a single image using a variety of methods.
mean = col.mean()
m.add_layer(mean, vis_refl, 'Mean (B11, B8, B3)')
median = col.median()
m.add_layer(median, vis_refl, 'Median (B11, B8, B3)')
min = col.min()
m.add_layer(min, vis_refl, 'Min (B11, B8, B3)')
max = col.max()
m.add_layer(max, vis_refl, 'Max (B11, B8, B3)')
sum = col.sum()
m.add_layer(
sum, {'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 500}, 'Sum (MSK_CLDPRB)'
)
product = col.product()
m.add_layer(
product,
{'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 1e10},
'Product (MSK_CLDPRB)',
)
# ee.ImageCollection.mode returns the most common value. If multiple mode
# values occur, the minimum mode value is returned.
mode = col.mode()
m.add_layer(
mode, {'bands': ['SCL'], 'min': 1, 'max': 11}, 'Mode (pixel class)'
)
# ee.ImageCollection.count returns the frequency of valid observations. Here,
# image pixels are masked based on cloud probability to add valid observation
# variability to the collection. Note that pixels with no valid observations
# are masked out of the returned image.
not_cloud_col = col.map(
lambda img: img.updateMask(img.select('MSK_CLDPRB').lte(10))
)
count = not_cloud_col.count()
m.add_layer(count, {'min': 1, 'max': 5}, 'Count (not cloud observations)')
# ee.ImageCollection.mosaic composites images according to their position in
# the collection (priority is last to first) and pixel mask status, where
# invalid (mask value 0) pixels are filled by preceding valid (mask value >0)
# pixels.
mosaic = not_cloud_col.mosaic()
m.add_layer(mosaic, vis_refl, 'Mosaic (B11, B8, B3)')
m،با محاسبه رایج‌ترین مقدار در هر پیکسل در پشته همه باندهای منطبق، یک مجموعه تصویر را کاهش می‌دهد. گروه ها با نام مطابقت دارند.
| استفاده | برمی گرداند | ImageCollection. mode () | تصویر |
| استدلال | تایپ کنید | جزئیات | این: collection | ImageCollection | مجموعه تصاویر برای کاهش. |
نمونه ها
ویرایشگر کد (جاوا اسکریپت)
// Sentinel-2 image collection for July 2021 intersecting a point of interest.
// Reflectance, cloud probability, and scene classification bands are selected.
var col = ee.ImageCollection('COPERNICUS/S2_SR')
.filterDate('2021-07-01', '2021-08-01')
.filterBounds(ee.Geometry.Point(-122.373, 37.448))
.select('B.*|MSK_CLDPRB|SCL');
// Visualization parameters for reflectance RGB.
var visRefl = {
bands: ['B11', 'B8', 'B3'],
min: 0,
max: 4000
};
Map.setCenter(-122.373, 37.448, 9);
Map.addLayer(col, visRefl, 'Collection reference', false);
// Reduce the collection to a single image using a variety of methods.
var mean = col.mean();
Map.addLayer(mean, visRefl, 'Mean (B11, B8, B3)');
var median = col.median();
Map.addLayer(median, visRefl, 'Median (B11, B8, B3)');
var min = col.min();
Map.addLayer(min, visRefl, 'Min (B11, B8, B3)');
var max = col.max();
Map.addLayer(max, visRefl, 'Max (B11, B8, B3)');
var sum = col.sum();
Map.addLayer(sum,
{bands: ['MSK_CLDPRB'], min: 0, max: 500}, 'Sum (MSK_CLDPRB)');
var product = col.product();
Map.addLayer(product,
{bands: ['MSK_CLDPRB'], min: 0, max: 1e10}, 'Product (MSK_CLDPRB)');
// ee.ImageCollection.mode returns the most common value. If multiple mode
// values occur, the minimum mode value is returned.
var mode = col.mode();
Map.addLayer(mode, {bands: ['SCL'], min: 1, max: 11}, 'Mode (pixel class)');
// ee.ImageCollection.count returns the frequency of valid observations. Here,
// image pixels are masked based on cloud probability to add valid observation
// variability to the collection. Note that pixels with no valid observations
// are masked out of the returned image.
var notCloudCol = col.map(function(img) {
return img.updateMask(img.select('MSK_CLDPRB').lte(10));
});
var count = notCloudCol.count();
Map.addLayer(count, {min: 1, max: 5}, 'Count (not cloud observations)');
// ee.ImageCollection.mosaic composites images according to their position in
// the collection (priority is last to first) and pixel mask status, where
// invalid (mask value 0) pixels are filled by preceding valid (mask value >0)
// pixels.
var mosaic = notCloudCol.mosaic();
Map.addLayer(mosaic, visRefl, 'Mosaic (B11, B8, B3)'); راه اندازی پایتون
برای اطلاعات در مورد API پایتون و استفاده از geemap برای توسعه تعاملی به صفحه محیط پایتون مراجعه کنید.
import ee
import geemap.core as geemap
کولب (پایتون)
# Sentinel-2 image collection for July 2021 intersecting a point of interest.
# Reflectance, cloud probability, and scene classification bands are selected.
col = (
ee.ImageCollection('COPERNICUS/S2_SR')
.filterDate('2021-07-01', '2021-08-01')
.filterBounds(ee.Geometry.Point(-122.373, 37.448))
.select('B.*|MSK_CLDPRB|SCL')
)
# Visualization parameters for reflectance RGB.
vis_refl = {'bands': ['B11', 'B8', 'B3'], 'min': 0, 'max': 4000}
m = geemap.Map()
m.set_center(-122.373, 37.448, 9)
m.add_layer(col, vis_refl, 'Collection reference', False)
# Reduce the collection to a single image using a variety of methods.
mean = col.mean()
m.add_layer(mean, vis_refl, 'Mean (B11, B8, B3)')
median = col.median()
m.add_layer(median, vis_refl, 'Median (B11, B8, B3)')
min = col.min()
m.add_layer(min, vis_refl, 'Min (B11, B8, B3)')
max = col.max()
m.add_layer(max, vis_refl, 'Max (B11, B8, B3)')
sum = col.sum()
m.add_layer(
sum, {'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 500}, 'Sum (MSK_CLDPRB)'
)
product = col.product()
m.add_layer(
product,
{'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 1e10},
'Product (MSK_CLDPRB)',
)
# ee.ImageCollection.mode returns the most common value. If multiple mode
# values occur, the minimum mode value is returned.
mode = col.mode()
m.add_layer(
mode, {'bands': ['SCL'], 'min': 1, 'max': 11}, 'Mode (pixel class)'
)
# ee.ImageCollection.count returns the frequency of valid observations. Here,
# image pixels are masked based on cloud probability to add valid observation
# variability to the collection. Note that pixels with no valid observations
# are masked out of the returned image.
not_cloud_col = col.map(
lambda img: img.updateMask(img.select('MSK_CLDPRB').lte(10))
)
count = not_cloud_col.count()
m.add_layer(count, {'min': 1, 'max': 5}, 'Count (not cloud observations)')
# ee.ImageCollection.mosaic composites images according to their position in
# the collection (priority is last to first) and pixel mask status, where
# invalid (mask value 0) pixels are filled by preceding valid (mask value >0)
# pixels.
mosaic = not_cloud_col.mosaic()
m.add_layer(mosaic, vis_refl, 'Mosaic (B11, B8, B3)')
m
جز در مواردی که غیر از این ذکر شده باشد،محتوای این صفحه تحت مجوز Creative Commons Attribution 4.0 License است. نمونه کدها نیز دارای مجوز Apache 2.0 License است. برای اطلاع از جزئیات، به خطمشیهای سایت Google Developers مراجعه کنید. جاوا علامت تجاری ثبتشده Oracle و/یا شرکتهای وابسته به آن است.
تاریخ آخرین بهروزرسانی 2025-07-24 بهوقت ساعت هماهنگ جهانی.
[null,null,["تاریخ آخرین بهروزرسانی 2025-07-24 بهوقت ساعت هماهنگ جهانی."],[[["\u003cp\u003e\u003ccode\u003eImageCollection.mode()\u003c/code\u003e reduces an image collection to a single image by calculating the most frequent pixel value for each band across the collection.\u003c/p\u003e\n"],["\u003cp\u003eBands are matched by name during the reduction process.\u003c/p\u003e\n"],["\u003cp\u003eIf multiple pixel values have the same highest frequency (multiple modes), the minimum mode value is selected for that pixel.\u003c/p\u003e\n"],["\u003cp\u003eThe resulting image represents the most common values observed in the collection for each band.\u003c/p\u003e\n"]]],["The content details the `mode()` function within an `ImageCollection`, which finds the most frequent pixel value across matching bands in a stack of images, returning a single `Image`. It demonstrates reducing an image collection using functions such as: `mean()`, `median()`, `min()`, `max()`, `sum()`, and `product()`. It also shows how to calculate a pixel frequency using `count()` and to mosaic together images with `mosaic()`. `mode` returns the lowest value if multiple values have the same frequency.\n"],null,["# ee.ImageCollection.mode\n\nReduces an image collection by calculating the most common value at each pixel across the stack of all matching bands. Bands are matched by name.\n\n\u003cbr /\u003e\n\n| Usage | Returns |\n|--------------------------|---------|\n| ImageCollection.mode`()` | Image |\n\n| Argument | Type | Details |\n|--------------------|-----------------|---------------------------------|\n| this: `collection` | ImageCollection | The image collection to reduce. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\n// Sentinel-2 image collection for July 2021 intersecting a point of interest.\n// Reflectance, cloud probability, and scene classification bands are selected.\nvar col = ee.ImageCollection('COPERNICUS/S2_SR')\n .filterDate('2021-07-01', '2021-08-01')\n .filterBounds(ee.Geometry.Point(-122.373, 37.448))\n .select('B.*|MSK_CLDPRB|SCL');\n\n// Visualization parameters for reflectance RGB.\nvar visRefl = {\n bands: ['B11', 'B8', 'B3'],\n min: 0,\n max: 4000\n};\nMap.setCenter(-122.373, 37.448, 9);\nMap.addLayer(col, visRefl, 'Collection reference', false);\n\n// Reduce the collection to a single image using a variety of methods.\nvar mean = col.mean();\nMap.addLayer(mean, visRefl, 'Mean (B11, B8, B3)');\n\nvar median = col.median();\nMap.addLayer(median, visRefl, 'Median (B11, B8, B3)');\n\nvar min = col.min();\nMap.addLayer(min, visRefl, 'Min (B11, B8, B3)');\n\nvar max = col.max();\nMap.addLayer(max, visRefl, 'Max (B11, B8, B3)');\n\nvar sum = col.sum();\nMap.addLayer(sum,\n {bands: ['MSK_CLDPRB'], min: 0, max: 500}, 'Sum (MSK_CLDPRB)');\n\nvar product = col.product();\nMap.addLayer(product,\n {bands: ['MSK_CLDPRB'], min: 0, max: 1e10}, 'Product (MSK_CLDPRB)');\n\n// ee.ImageCollection.mode returns the most common value. If multiple mode\n// values occur, the minimum mode value is returned.\nvar mode = col.mode();\nMap.addLayer(mode, {bands: ['SCL'], min: 1, max: 11}, 'Mode (pixel class)');\n\n// ee.ImageCollection.count returns the frequency of valid observations. Here,\n// image pixels are masked based on cloud probability to add valid observation\n// variability to the collection. Note that pixels with no valid observations\n// are masked out of the returned image.\nvar notCloudCol = col.map(function(img) {\n return img.updateMask(img.select('MSK_CLDPRB').lte(10));\n});\nvar count = notCloudCol.count();\nMap.addLayer(count, {min: 1, max: 5}, 'Count (not cloud observations)');\n\n// ee.ImageCollection.mosaic composites images according to their position in\n// the collection (priority is last to first) and pixel mask status, where\n// invalid (mask value 0) pixels are filled by preceding valid (mask value \u003e0)\n// pixels.\nvar mosaic = notCloudCol.mosaic();\nMap.addLayer(mosaic, visRefl, 'Mosaic (B11, B8, B3)');\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\n# Sentinel-2 image collection for July 2021 intersecting a point of interest.\n# Reflectance, cloud probability, and scene classification bands are selected.\ncol = (\n ee.ImageCollection('COPERNICUS/S2_SR')\n .filterDate('2021-07-01', '2021-08-01')\n .filterBounds(ee.Geometry.Point(-122.373, 37.448))\n .select('B.*|MSK_CLDPRB|SCL')\n)\n\n# Visualization parameters for reflectance RGB.\nvis_refl = {'bands': ['B11', 'B8', 'B3'], 'min': 0, 'max': 4000}\nm = geemap.Map()\nm.set_center(-122.373, 37.448, 9)\nm.add_layer(col, vis_refl, 'Collection reference', False)\n\n# Reduce the collection to a single image using a variety of methods.\nmean = col.mean()\nm.add_layer(mean, vis_refl, 'Mean (B11, B8, B3)')\n\nmedian = col.median()\nm.add_layer(median, vis_refl, 'Median (B11, B8, B3)')\n\nmin = col.min()\nm.add_layer(min, vis_refl, 'Min (B11, B8, B3)')\n\nmax = col.max()\nm.add_layer(max, vis_refl, 'Max (B11, B8, B3)')\n\nsum = col.sum()\nm.add_layer(\n sum, {'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 500}, 'Sum (MSK_CLDPRB)'\n)\n\nproduct = col.product()\nm.add_layer(\n product,\n {'bands': ['MSK_CLDPRB'], 'min': 0, 'max': 1e10},\n 'Product (MSK_CLDPRB)',\n)\n\n# ee.ImageCollection.mode returns the most common value. If multiple mode\n# values occur, the minimum mode value is returned.\nmode = col.mode()\nm.add_layer(\n mode, {'bands': ['SCL'], 'min': 1, 'max': 11}, 'Mode (pixel class)'\n)\n\n# ee.ImageCollection.count returns the frequency of valid observations. Here,\n# image pixels are masked based on cloud probability to add valid observation\n# variability to the collection. Note that pixels with no valid observations\n# are masked out of the returned image.\nnot_cloud_col = col.map(\n lambda img: img.updateMask(img.select('MSK_CLDPRB').lte(10))\n)\ncount = not_cloud_col.count()\nm.add_layer(count, {'min': 1, 'max': 5}, 'Count (not cloud observations)')\n\n# ee.ImageCollection.mosaic composites images according to their position in\n# the collection (priority is last to first) and pixel mask status, where\n# invalid (mask value 0) pixels are filled by preceding valid (mask value \u003e0)\n# pixels.\nmosaic = not_cloud_col.mosaic()\nm.add_layer(mosaic, vis_refl, 'Mosaic (B11, B8, B3)')\nm\n```"]]
